Release: v0.22.3

* fix mask feature condition.

* debug

* remove identical test

* set correct

* Empty-Commit

examples/controlnet/train_controlnet.py

@@ -56,7 +56,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/controlnet/train_controlnet_flax.py

@@ -59,7 +59,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = logging.getLogger(__name__)
 

examples/controlnet/train_controlnet_sdxl.py

@@ -58,7 +58,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/custom_diffusion/train_custom_diffusion.py

@@ -62,7 +62,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth.py

@@ -61,7 +61,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_flax.py

@@ -35,7 +35,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 # Cache compiled models across invocations of this script.
 cc.initialize_cache(os.path.expanduser("~/.cache/jax/compilation_cache"))

examples/dreambooth/train_dreambooth_lora.py

@@ -68,7 +68,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -58,7 +58,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -52,7 +52,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/instruct_pix2pix/train_instruct_pix2pix_sdxl.py

@@ -55,7 +55,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py

@@ -52,7 +52,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py

@@ -46,7 +46,7 @@ from diffusers.utils import check_min_version, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py

@@ -51,7 +51,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.21.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -58,7 +58,7 @@ if is_wandb_available():
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image.py

@@ -53,7 +53,7 @@ if is_wandb_available():
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_flax.py

@@ -33,7 +33,7 @@ from diffusers.utils import check_min_version
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = logging.getLogger(__name__)
 

examples/text_to_image/train_text_to_image_lora.py

@@ -49,7 +49,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -58,7 +58,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/text_to_image/train_text_to_image_sdxl.py

@@ -57,7 +57,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion.py

@@ -79,7 +79,7 @@ else:
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__)
 

examples/textual_inversion/textual_inversion_flax.py

@@ -56,7 +56,7 @@ else:
 # ------------------------------------------------------------------------------
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = logging.getLogger(__name__)
 

examples/unconditional_image_generation/train_unconditional.py

@@ -29,7 +29,7 @@ from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.22.0.dev0")
+check_min_version("0.22.0")
 
 logger = get_logger(__name__, log_level="INFO")
 

setup.py

@@ -244,7 +244,7 @@ install_requires = [
 
 setup(
     name="diffusers",
-    version="0.22.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="0.22.3",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     description="State-of-the-art diffusion in PyTorch and JAX.",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

src/diffusers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.22.0.dev0"
+__version__ = "0.22.3"
 
 from typing import TYPE_CHECKING
 

src/diffusers/loaders.py

@@ -2390,7 +2390,7 @@ class LoraLoaderMixin:
     def set_adapters_for_text_encoder(
         self,
         adapter_names: Union[List[str], str],
-        text_encoder: Optional[PreTrainedModel] = None,
+        text_encoder: Optional["PreTrainedModel"] = None,  # noqa: F821
         text_encoder_weights: List[float] = None,
     ):
         """
@@ -2429,7 +2429,7 @@ class LoraLoaderMixin:
             )
         set_weights_and_activate_adapters(text_encoder, adapter_names, text_encoder_weights)
 
-    def disable_lora_for_text_encoder(self, text_encoder: Optional[PreTrainedModel] = None):
+    def disable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
         """
         Disables the LoRA layers for the text encoder.
 
@@ -2446,7 +2446,7 @@ class LoraLoaderMixin:
             raise ValueError("Text Encoder not found.")
         set_adapter_layers(text_encoder, enabled=False)
 
-    def enable_lora_for_text_encoder(self, text_encoder: Optional[PreTrainedModel] = None):
+    def enable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
         """
         Enables the LoRA layers for the text encoder.
 

src/diffusers/models/attention.py

@@ -287,7 +287,7 @@ class BasicTransformerBlock(nn.Module):
             else:
                 raise ValueError("Incorrect norm")
 
-            if self.pos_embed is not None and self.use_ada_layer_norm_single is None:
+            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
                 norm_hidden_states = self.pos_embed(norm_hidden_states)
 
             attn_output = self.attn2(

src/diffusers/models/attention_processor.py

@@ -378,7 +378,7 @@ class Attention(nn.Module):
             _remove_lora (`bool`, *optional*, defaults to `False`):
                 Set to `True` to remove LoRA layers from the model.
         """
-        if hasattr(self, "processor") and _remove_lora and self.to_q.lora_layer is not None:
+        if not USE_PEFT_BACKEND and hasattr(self, "processor") and _remove_lora and self.to_q.lora_layer is not None:
             deprecate(
                 "set_processor to offload LoRA",
                 "0.26.0",

src/diffusers/models/transformer_2d.py

@@ -339,6 +339,7 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         elif self.is_input_vectorized:
             hidden_states = self.latent_image_embedding(hidden_states)
         elif self.is_input_patches:
+            height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
             hidden_states = self.pos_embed(hidden_states)
 
             if self.adaln_single is not None:
@@ -425,7 +426,8 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
                 hidden_states = hidden_states.squeeze(1)
 
             # unpatchify
-            height = width = int(hidden_states.shape[1] ** 0.5)
+            if self.adaln_single is None:
+                height = width = int(hidden_states.shape[1] ** 0.5)
             hidden_states = hidden_states.reshape(
                 shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
             )

src/diffusers/pipelines/latent_consistency_models/__init__.py

@@ -1,19 +1,40 @@
 from typing import TYPE_CHECKING
 
 from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
     _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
 )
 
 
-_import_structure = {
-    "pipeline_latent_consistency_img2img": ["LatentConsistencyModelImg2ImgPipeline"],
-    "pipeline_latent_consistency_text2img": ["LatentConsistencyModelPipeline"],
-}
+_dummy_objects = {}
+_import_structure = {}
 
 
-if TYPE_CHECKING:
-    from .pipeline_latent_consistency_img2img import LatentConsistencyModelImg2ImgPipeline
-    from .pipeline_latent_consistency_text2img import LatentConsistencyModelPipeline
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_latent_consistency_img2img"] = ["LatentConsistencyModelImg2ImgPipeline"]
+    _import_structure["pipeline_latent_consistency_text2img"] = ["LatentConsistencyModelPipeline"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_latent_consistency_img2img import LatentConsistencyModelImg2ImgPipeline
+        from .pipeline_latent_consistency_text2img import LatentConsistencyModelPipeline
 
 else:
     import sys
@@ -24,3 +45,6 @@ else:
         _import_structure,
         module_spec=__spec__,
     )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -738,7 +738,7 @@ class LatentConsistencyModelImg2ImgPipeline(
             if original_inference_steps is not None
             else self.scheduler.config.original_inference_steps
         )
-        latent_timestep = torch.tensor(int(strength * original_inference_steps))
+        latent_timestep = timesteps[:1]
         latents = self.prepare_latents(
             image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
         )

src/diffusers/pipelines/pipeline_utils.py

@@ -353,13 +353,18 @@ def _get_pipeline_class(
         else:
             file_name = CUSTOM_PIPELINE_FILE_NAME
 
+        if repo_id is not None and hub_revision is not None:
+            # if we load the pipeline code from the Hub
+            # make sure to overwrite the `revison`
+            revision = hub_revision
+
         return get_class_from_dynamic_module(
             custom_pipeline,
             module_file=file_name,
             class_name=class_name,
             repo_id=repo_id,
             cache_dir=cache_dir,
-            revision=revision if hub_revision is None else hub_revision,
+            revision=revision,
         )
 
     if class_obj != DiffusionPipeline:

src/diffusers/pipelines/pixart_alpha/__init__.py

@@ -1 +1,48 @@
-from .pipeline_pixart_alpha import PixArtAlphaPipeline
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_pixart_alpha"] = ["PixArtAlphaPipeline"]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_pixart_alpha import PixArtAlphaPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py

@@ -156,6 +156,8 @@ class PixArtAlphaPipeline(DiffusionPipeline):
             mask_feature: (bool, defaults to `True`):
                 If `True`, the function will mask the text embeddings.
         """
+        embeds_initially_provided = prompt_embeds is not None and negative_prompt_embeds is not None
+
         if device is None:
             device = self._execution_device
 
@@ -253,7 +255,7 @@ class PixArtAlphaPipeline(DiffusionPipeline):
             negative_prompt_embeds = None
 
         # Perform additional masking.
-        if mask_feature:
+        if mask_feature and not embeds_initially_provided:
             prompt_embeds = prompt_embeds.unsqueeze(1)
             masked_prompt_embeds, keep_indices = self.mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
             masked_prompt_embeds = masked_prompt_embeds.squeeze(1)

tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py

@@ -133,7 +133,7 @@ class LatentConsistencyModelImg2ImgPipelineFastTests(
         assert image.shape == (1, 32, 32, 3)
 
         image_slice = image[0, -3:, -3:, -1]
-        expected_slice = np.array([0.5865, 0.2854, 0.2828, 0.7473, 0.6006, 0.4580, 0.4397, 0.6415, 0.6069])
+        expected_slice = np.array([0.4388, 0.3717, 0.2202, 0.7213, 0.6370, 0.3664, 0.5815, 0.6080, 0.4977])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
     def test_lcm_multistep(self):
@@ -150,7 +150,7 @@ class LatentConsistencyModelImg2ImgPipelineFastTests(
         assert image.shape == (1, 32, 32, 3)
 
         image_slice = image[0, -3:, -3:, -1]
-        expected_slice = np.array([0.4903, 0.3304, 0.3503, 0.5241, 0.5153, 0.4585, 0.3222, 0.4764, 0.4891])
+        expected_slice = np.array([0.4150, 0.3719, 0.2479, 0.6333, 0.6024, 0.3778, 0.5036, 0.5420, 0.4678])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
     def test_inference_batch_single_identical(self):
@@ -237,7 +237,7 @@ class LatentConsistencyModelImg2ImgPipelineSlowTests(unittest.TestCase):
         assert image.shape == (1, 512, 512, 3)
 
         image_slice = image[0, -3:, -3:, -1].flatten()
-        expected_slice = np.array([0.1025, 0.0911, 0.0984, 0.0981, 0.0901, 0.0918, 0.1055, 0.0940, 0.0730])
+        expected_slice = np.array([0.1950, 0.1961, 0.2308, 0.1786, 0.1837, 0.2320, 0.1898, 0.1885, 0.2309])
         assert np.abs(image_slice - expected_slice).max() < 1e-3
 
     def test_lcm_multistep(self):
@@ -253,5 +253,5 @@ class LatentConsistencyModelImg2ImgPipelineSlowTests(unittest.TestCase):
         assert image.shape == (1, 512, 512, 3)
 
         image_slice = image[0, -3:, -3:, -1].flatten()
-        expected_slice = np.array([0.01855, 0.01855, 0.01489, 0.01392, 0.01782, 0.01465, 0.01831, 0.02539, 0.0])
+        expected_slice = np.array([0.3756, 0.3816, 0.3767, 0.3718, 0.3739, 0.3735, 0.3863, 0.3803, 0.3563])
         assert np.abs(image_slice - expected_slice).max() < 1e-3

tests/pipelines/pixart/test_pixart.py

@@ -120,7 +120,6 @@ class PixArtAlphaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "generator": generator,
             "num_inference_steps": num_inference_steps,
             "output_type": output_type,
-            "mask_feature": False,
         }
 
         # set all optional components to None
@@ -155,7 +154,6 @@ class PixArtAlphaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "generator": generator,
             "num_inference_steps": num_inference_steps,
             "output_type": output_type,
-            "mask_feature": False,
         }
 
         output_loaded = pipe_loaded(**inputs)[0]
@@ -174,18 +172,99 @@ class PixArtAlphaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         inputs = self.get_dummy_inputs(device)
         image = pipe(**inputs).images
         image_slice = image[0, -3:, -3:, -1]
-        print(torch.from_numpy(image_slice.flatten()))
 
         self.assertEqual(image.shape, (1, 8, 8, 3))
         expected_slice = np.array([0.5303, 0.2658, 0.7979, 0.1182, 0.3304, 0.4608, 0.5195, 0.4261, 0.4675])
         max_diff = np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(max_diff, 1e-3)
 
+    def test_inference_non_square_images(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = pipe(**inputs, height=32, width=48).images
+        image_slice = image[0, -3:, -3:, -1]
+
+        self.assertEqual(image.shape, (1, 32, 48, 3))
+        expected_slice = np.array([0.3859, 0.2987, 0.2333, 0.5243, 0.6721, 0.4436, 0.5292, 0.5373, 0.4416])
+        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
+        self.assertLessEqual(max_diff, 1e-3)
+
+    def test_inference_with_embeddings_and_multiple_images(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        prompt = inputs["prompt"]
+        generator = inputs["generator"]
+        num_inference_steps = inputs["num_inference_steps"]
+        output_type = inputs["output_type"]
+
+        prompt_embeds, negative_prompt_embeds = pipe.encode_prompt(prompt)
+
+        # inputs with prompt converted to embeddings
+        inputs = {
+            "prompt_embeds": prompt_embeds,
+            "negative_prompt": None,
+            "negative_prompt_embeds": negative_prompt_embeds,
+            "generator": generator,
+            "num_inference_steps": num_inference_steps,
+            "output_type": output_type,
+            "num_images_per_prompt": 2,
+        }
+
+        # set all optional components to None
+        for optional_component in pipe._optional_components:
+            setattr(pipe, optional_component, None)
+
+        output = pipe(**inputs)[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
+            pipe_loaded.to(torch_device)
+            pipe_loaded.set_progress_bar_config(disable=None)
+
+        for optional_component in pipe._optional_components:
+            self.assertTrue(
+                getattr(pipe_loaded, optional_component) is None,
+                f"`{optional_component}` did not stay set to None after loading.",
+            )
+
+        inputs = self.get_dummy_inputs(torch_device)
+
+        generator = inputs["generator"]
+        num_inference_steps = inputs["num_inference_steps"]
+        output_type = inputs["output_type"]
+
+        # inputs with prompt converted to embeddings
+        inputs = {
+            "prompt_embeds": prompt_embeds,
+            "negative_prompt": None,
+            "negative_prompt_embeds": negative_prompt_embeds,
+            "generator": generator,
+            "num_inference_steps": num_inference_steps,
+            "output_type": output_type,
+            "num_images_per_prompt": 2,
+        }
+
+        output_loaded = pipe_loaded(**inputs)[0]
+
+        max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
+        self.assertLess(max_diff, 1e-4)
+
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=1e-3)
 
 
-# TODO: needs to be updated.
 @slow
 @require_torch_gpu
 class PixArtAlphaPipelineIntegrationTests(unittest.TestCase):